(492d) A Parallel Biologically-Inspired Optimal Control Strategy (BIO-CS) for Model Predictive Control of Advanced Energy Systems

The Biologically-Inspired Optimal Control Strategy (BIO-CS) for process control applications was derived from the behavior of swarm intelligence found in nature. This behavior is commonly demonstrated by ants and bees to complete collaborative tasks. In particular, the BIO-CS approach for control employs agents that cooperate to solve a model predictive control problem to obtain optimal control profiles. The optimization in BIO-CS is achieved by refining the trajectory of the agents until a consistent optimal trajectory is obtained¹. However, tractability challenges may be encountered when solving the BIO-CS optimization problem online², especially when tackling highly integrated advanced energy systems, including power systems¹. In this work, a parallel BIO-CS approach is proposed for obtaining and evaluating the agent trajectories for use in model predictive control of advanced energy systems.

The parallel control strategy will employ a modified ant’s rule of pursuit strategy, in which multiple optimal control problems associated with different sets of agents will be solved in parallel. Multi-agent optimization as well as gradient-based approaches will be considered for the solution of the optimal control problems aiming for enhanced computational time efficiency³. The new control strategy will be demonstrated on different energy system models, including components of a subcritical coal-fired power plant and of a hybrid energy system^4,5. For the subcritical coal-fired power plant, models for a plant capacity of 400 MW with appropriate dynamics have been developed. To assess the performance and feasibility of the algorithm to be implemented in an actual power plant, publicly available online load demand profiles from different parts of the United States will be employed^6,7,8. The hybrid energy system is modeled to allow the integration of a gas turbine and a fuel cell system. For the hybrid energy system, the typical turbine speed range will be used as test signals for setpoint and disturbance rejection scenarios.

Simulation studies will be performed and discussed to analyze and contrast the traditional and parallel BIO-CS algorithms for optimal control operations considering setpoint tracking and disturbance rejection case studies. These studies will aim to assess the potential implementation of BIO-CS in industry for achieving optimal and computationally efficient control of energy systems including the highly integrated and hybrid systems of the future.

References:

¹ Mirlekar, G., Al-Sinbol, G., Perhinschi, M., & Lima, F. V. (2018). A biologically-inspired approach for adaptive control of advanced energy systems. Computers & Chemical Engineering, 117, 378-390. doi:10.1016/j.compchemeng.2018.07.002

² Mirlekar, G., Li, S., & Lima, F. V. (2017). Design and Implementation of a Biologically Inspired Optimal Control Strategy for Chemical Process Control. Industrial & Engineering Chemistry Research, 56(22), 6468–6479. https://doi.org/10.1021/acs.iecr.6b04753

³ Mirlekar, G., Gebreslassie, B., Diwekar, U., & Lima, F. V. (2018). Biomimetic model-based advanced control strategy integrated with multi-agent optimization for nonlinear chemical processes. Chemical Engineering Research and Design, 140, 229-240. doi:10.1016/j.cherd.2018.10.005

⁴ Agbleze, S., Lima, F. V., Indrawan, N., Panday, R., Pezzini, P., Bonilla-Alvarado, H., Bryden, K. M., Tucker, D., & Shadle, L. J. (2020). Modeling and Control of Subcritical Coal-Fired Power Plant Components for Fault Detection. ASME 2020 Power Conference. https://doi.org/10.1115/power2020-16571

⁵ Mirlekar, G., Pezzini, P., Bryden, M., Tucker, D., & Lima, F. V. (2017). A Biologically-Inspired Optimal Control Strategy (BIO-CS) for hybrid energy systems. 2017 American Control Conference (ACC). https://doi.org/10.23919/acc.2017.7963701

⁶ Electric Reliability Council of Texas (ERCOT). (n.d.). Retrieved April 10, 2021, from http://www.ercot.com/

⁷ Pennsylvania, New Jersey, and Maryland (PJM) Markets & Operations. (n.d.). Retrieved April 10, 2021, from http://www.pjm.com/

⁸ U.S. Energy Information Administration (EIA). (n.d.). Retrieved April 10, 2021, from https://www.eia.gov/